Displaying similar documents to “Improving prediction of neural networks: a study of two financial prediction tasks.”

Neural networks as a tool for georadar data processing

Piotr Szymczyk, Sylwia Tomecka-Suchoń, Magdalena Szymczyk (2015)

International Journal of Applied Mathematics and Computer Science

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In this article a new neural network based method for automatic classification of ground penetrating radar (GPR) traces is proposed. The presented approach is based on a new representation of GPR signals by polynomials approximation. The coefficients of the polynomial (the feature vector) are neural network inputs for automatic classification of a special kind of geologic structure-a sinkhole. The analysis and results show that the classifier can effectively distinguish sinkholes from...

A heuristic forecasting model for stock decision making.

D. Zhang, Q. Jiang, X. Li (2005)

Mathware and Soft Computing

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This paper describes a heuristic forecasting model based on neural networks for stock decision-making. Some heuristic strategies are presented for enhancing the learning capability of neural networks and obtaining better trading performance. The China Shanghai Composite Index is used as case study. The forecasting model can forecast the buying and selling signs according to the result of neural network prediction. Results are compared with a benchmark buy-and-hold strategy. The forecasting...

Backpropagation generalized delta rule for the selective attention Sigma-if artificial neural network

Maciej Huk (2012)

International Journal of Applied Mathematics and Computer Science

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In this paper the Sigma-if artificial neural network model is considered, which is a generalization of an MLP network with sigmoidal neurons. It was found to be a potentially universal tool for automatic creation of distributed classification and selective attention systems. To overcome the high nonlinearity of the aggregation function of Sigma-if neurons, the training process of the Sigma-if network combines an error backpropagation algorithm with the self-consistency paradigm widely...

Artificial neural networks in time series forecasting: a comparative analysis

Héctor Allende, Claudio Moraga, Rodrigo Salas (2002)

Kybernetika

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Artificial neural networks (ANN) have received a great deal of attention in many fields of engineering and science. Inspired by the study of brain architecture, ANN represent a class of non-linear models capable of learning from data. ANN have been applied in many areas where statistical methods are traditionally employed. They have been used in pattern recognition, classification, prediction and process control. The purpose of this paper is to discuss ANN and compare them to non-linear...